mlsanityData to 7 Oct 2026

Inverse Airborne Optical Sectioning

arXiv 2207.13344 ↗First posted 27 Jul 2022cs.CV eess.IVcited by 9

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Abstract (arXiv)

We present Inverse Airborne Optical Sectioning (IAOS) an optical analogy to Inverse Synthetic Aperture Radar (ISAR). Moving targets, such as walking people, that are heavily occluded by vegetation can be made visible and tracked with a stationary optical sensor (e.g., a hovering camera drone above forest). We introduce the principles of IAOS (i.e., inverse synthetic aperture imaging), explain how the signal of occluders can be further suppressed by filtering the Radon transform of the image integral, and present how targets motion parameters can be estimated manually and automatically. Finally, we show that while tracking occluded targets in conventional aerial images is infeasible, it becomes efficiently possible in integral images that result from IAOS.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 9 of its 72 references that are in the map. First posted on arXiv 27 Jul 2022.

Builds on

9 of 72 references.

References · 9

  1. 2021An Autonomous Drone for Search and Rescue in Forests using Airborne Optical Sectioningcited by 31
  2. 2020Search and Rescue with Airborne Optical Sectioningcited by 20
  3. 2019A Statistical View on Synthetic Aperture Imaging for Occlusion Removalcited by 15
  4. 2020Fast Automatic Visibility Optimization for Thermal Synthetic Aperture Visualizationcited by 14
  5. 2020Pose Error Reduction for Focus Enhancement in Thermal Synthetic Aperture Visualizationcited by 13
  6. 2021Combined Person Classification with Airborne Optical Sectioningcited by 13
  7. 2021Through-Foliage Tracking with Airborne Optical Sectioningcited by 13
  8. 2022On the Role of Field of View for Occlusion Removal with Airborne Optical Sectioningcited by 6
  9. 2021Classification Of Automotive Targets Using Inverse Synthetic Aperture Radar Imagescited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 1; 2025: 2; 2026: 1.

9 papers.